Val Piora Wheatear: Untitled

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library(GeoPressureR)
library(leaflet)
library(leaflet.extras)
library(raster)
library(dplyr)
library(ggplot2)
library(kableExtra)
library(plotly)
library(GeoLocTools)
setupGeolocation()
knitr::opts_chunk$set(echo = FALSE)
load(paste0("../data/1_pressure/", params$gdl_id, "_pressure_prob.Rdata"))
load(paste0("../data/2_light/", params$gdl_id, "_light_prob.Rdata"))
load(paste0("../data/3_static/", params$gdl_id, "_static_prob.Rdata"))
load(paste0("../data/4_basic_graph/", params$gdl_id, "_basic_graph.Rdata"))
load(paste0("../data/5_wind_graph/", params$gdl_id, "_wind_graph.Rdata"))
col <- rep(RColorBrewer::brewer.pal(8, "Dark2"), times = ceiling(max(pam$sta$sta_id) / 8))

Settings used

All the results produced here are generated with (1) the raw geolocator data, (2) the labeled files of pressure and light and (3) the parameters listed below.

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kable(gpr) %>% scroll_box(width = "100%")

gdl_id	keep	crop_start	crop_end	thr_dur	extent_N	extent_W	extent_S	extent_E	map_scale	map_max_sample	map_margin	prob_map_s	prob_map_s_calib	prob_map_thr	shift_k	kernel_adjust	calib_lon	calib_lat	calib_1_start	calib_1_end	calib_2_start	calib_2_end	calib_2_lon	calib_2_lat	prob_light_w	thr_prob_percentile	thr_gs	thr_as	RingNo	scientific_name	common_name	mass	wing_span	Color	sta_id_winter
24EA	1	2019-07-20	2019-10-31 05:45:00	0	51	-18	5	16	4	300	30	1	1.3	0.9	0	1.4	6.96333	44.95177	2019-07-20	2019-09-16	NA	NA	NA	NA	0.09	0.95	120	100	LW59569	Oenanthe oenanthe	Northern wheatear	NA	NA	#AB63FA	NA

Pressure timeserie

The labeling of pressure data is illustrated with this figure. The black dots indicates the pressure datapoint not considered in the matching. Each stationary period is illustrated by a different colored line.

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pressure_na <- pam$pressure %>%
  mutate(obs = ifelse(isoutlier | sta_id == 0, NA, obs))
p <- ggplot() +
  geom_line(data = pam$pressure, aes(x = date, y = obs), colour = "grey") +
  # geom_point(data = subset(pam$pressure, isoutlier), aes(x = date, y = obs), colour = "black") +
  # geom_line(data = pressure_na, aes(x = date, y = obs, color = factor(sta_id)), size = 0.5) +
  geom_line(data = do.call("rbind", shortest_path_timeserie) %>% filter(sta_id > 0), aes(x = date, y = pressure0, col = factor(sta_id))) +
  theme_bw() +
  scale_colour_manual(values = col) +
  scale_y_continuous(name = "Pressure(hPa)")

ggplotly(p, dynamicTicks = T) %>% layout(showlegend = F)

Pressure calibration

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pressure_ts_bind <- do.call("rbind", shortest_path_timeserie) %>%
  filter(!is.na(sta_id))

pam$pressure %>%
  left_join(pressure_ts_bind %>% dplyr::select(c("date", "pressure0")), by = "date") %>%
  mutate(diff = ifelse(is.na(pressure0), 0, obs - pressure0)) %>%
  filter(sta_id > 0 & !isoutlier) %>%
  group_by(sta_id) %>%
  mutate(sta_id = paste0(sta_id, " (SD=", round(sd(diff), 2), " ; N=", n(), ")")) %>%
  ggplot(aes(x = diff)) +
  geom_histogram(aes(y = (..count..) / tapply(..count.., ..PANEL.., sum)[..PANEL..]), binwidth = .2) +
  facet_wrap(~sta_id) +
  scale_x_continuous(name = "Pressure Geolocator - best match ERA5 (hPa)") +
  scale_y_continuous(name = "Normalized histogram")

Light

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raw_geolight <- pam$light %>%
  transmute(
    Date = date,
    Light = obs
  )
lightImage(tagdata = raw_geolight, offset = 0)
tsimagePoints(twl$twilight,
  offset = 0, pch = 16, cex = 1.2,
  col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
)
abline(v = gpr$calib_2_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_2_end, lty = 2, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_end, lty = 2, col = "firebrick", lwd = 1.5)

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hist(z, freq = F)
lines(fit_z, col = "red")

The probability map resulting from light data alone can be seen below.

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li_s <- list()
l <- leaflet(width = "100%") %>%
  addProviderTiles(providers$Stamen.TerrainBackground) %>%
  addFullscreenControl()
for (i_r in seq_len(length(light_prob))) {
  i_s <- metadata(light_prob[[i_r]])$sta_id
  info <- pam$sta[pam$sta$sta_id == i_s, ]
  info_str <- paste0(i_s, " | ", info$start, "->", info$end)
  li_s <- append(li_s, info_str)
  l <- l %>% addRasterImage(light_prob[[i_r]], opacity = 0.8, colors = "OrRd", group = info_str)
}
l %>%
  addCircles(lng = gpr$calib_lon, lat = gpr$calib_lat, color = "black", opacity = 1) %>%
  addLayersControl(
    overlayGroups = li_s,
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  hideGroup(tail(li_s, length(li_s) - 1))

Light vs Pressure

We can compare light and pressure location at long stationary stopover (>5 days). By assuming the best match of the pressure to be the truth, we can plot the histogram of the zenith angle and compare to the fit of kernel density at the calibration site.

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 raw_geolight <- pam$light %>%
    transmute(
      Date = date,
      Light = obs
    )
 dur <- unlist(lapply(pressure_prob, function(x) difftime(metadata(x)$temporal_extent[2],metadata(x)$temporal_extent[1], units = "days" )))
  long_id <- which(dur>5)

  par(mfrow = c(2, 3))
  for (i_s in long_id){
    twl_fl <- twl %>%
      filter(!deleted) %>%
      filter(twilight>shortest_path_timeserie[[i_s]]$date[1] & twilight<tail(shortest_path_timeserie[[i_s]]$date,1))
    sun <-  solar(twl_fl$twilight)
    z_i <- refracted(zenith(sun, shortest_path_timeserie[[i_s]]$lon[1], shortest_path_timeserie[[i_s]]$lat[1]))
    hist(z_i, freq = F, main = paste0("sta_id=",i_s, " | ",nrow(twl_fl),"twls"))
    lines(fit_z, col = "red")
    xlab("Zenith angle")
  }

Similarly, we can plot the line of sunrise/sunset at the best match of pressure (yellow line) and compare to the raw and labeled light data.

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  lightImage(
    tagdata = raw_geolight,
    offset = gpr$shift_k / 60 / 60
  )
  tsimagePoints(twl$twilight,
                offset = gpr$shift_k / 60 / 60, pch = 16, cex = 1.2,
                col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
  )
  for (ts in shortest_path_timeserie){
    twl_fl <- twl %>%
      filter(twilight>ts$date[1] & twilight<tail(ts$date,1))
    if (nrow(twl_fl)>0){
    tsimageDeploymentLines(twl_fl$twilight,
                           lon = ts$lon[1], ts$lat[1],
                           offset = gpr$shift_k / 60 / 60, lwd = 3,col = adjustcolor("orange", alpha.f = 0.5))
      
    }
  }

Stationay period information

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read_csv(paste0("../reports/figure_print/table_transition/transition_", params$gdl_id, ".csv")) %>% kable() %>% scroll_box(width = "100%")

…1	sta_id_s	sta_id_t	flight_duration	as_m	as_s	gs_m	gs_s	ws_m	ws_s	dist_m	dist_s	ws_m_support	ws_m_drift	start_flight	end_flight	sunrise	sunset	as_sp	gs_sp	ws_sp	dist_sp	ws_sp_support	ws_sp_drift	alt_min	alt_max	alt_mean	alt_med	alt_sumdabsdiff	alt_sumposdiff	gdl_id
1	1	2	6.5	40.85163	19.32891	40.02772	20.00647	5.109686	0.8018363	260.18018	130.04204	-0.5062576	5.084545	2019-09-15 18:00:00	2019-09-16 00:30:00	2019-09-16 04:49:07	2019-09-15 18:13:34	35.34347	33.65606	5.121217	218.76440	-1.3400815	4.9427766	37.049643	3339.6057	2248.8738	2610.0430	7850.436	2612.6042	24EA
2	2	3	5.5	80.52282	16.28088	79.91931	15.95173	8.001190	2.1679086	439.55623	87.73453	-0.2052617	7.998557	2019-09-16 18:00:00	2019-09-16 23:30:00	2019-09-17 05:03:14	2019-09-16 18:15:01	85.95793	84.75540	6.072656	466.15473	-0.9935061	5.9908343	-3.506296	2374.5672	1284.8146	1763.1219	4744.191	2366.1171	24EA
3	3	4	4.5	75.91882	14.65826	71.79817	15.83220	10.930215	2.1379135	323.09178	71.24492	-3.4069095	10.385691	2019-09-17 04:00:00	2019-09-17 08:30:00	2019-09-17 05:01:58	2019-09-16 18:21:57	65.14055	63.37325	12.541117	285.17962	-0.5510405	12.5290047	-30.158310	2134.7383	921.2303	904.1934	4328.087	2164.8966	24EA
4	4	5	2.0	53.76916	15.80070	44.74897	14.81924	17.503973	2.0570275	89.49795	29.63848	-6.5058792	16.250003	2019-09-17 21:30:00	2019-09-17 23:30:00	2019-09-18 05:02:15	2019-09-17 18:15:37	53.88986	43.09364	15.053161	86.18727	-9.5194840	11.6609207	-34.741219	1739.8438	513.6896	307.8903	3543.787	1943.2090	24EA
5	5	6	2.0	37.50359	18.28044	35.57574	16.34625	5.911572	4.1957496	71.15149	32.69250	-1.4889248	5.720995	2019-09-19 20:30:00	2019-09-19 22:30:00	2019-09-20 05:04:05	2019-09-19 18:11:08	36.69691	27.73917	8.958584	55.47834	-8.9574713	0.1411681	599.590544	1688.8900	1010.7673	971.0152	1574.232	484.9328	24EA
6	6	7	7.0	31.20499	14.76192	25.13120	13.59390	9.987888	2.2411343	175.91841	95.15733	-4.8230153	8.746224	2019-10-16 17:30:00	2019-10-17 00:30:00	2019-10-17 05:25:38	2019-10-16 17:32:59	31.08194	23.99566	7.415674	167.96964	-6.9867435	2.4854844	610.661026	2253.9275	1386.3409	1134.8906	3779.367	1952.0829	24EA
7	7	8	12.0	46.81537	16.11553	57.78886	16.54388	12.785692	3.9419526	693.46633	198.52657	11.3460242	5.894206	2019-10-18 17:30:00	2019-10-19 05:30:00	2019-10-19 05:30:46	2019-10-18 17:32:28	36.37411	50.44613	16.495161	605.35356	14.8061553	7.2710449	444.009498	3662.1946	2508.2101	2552.5553	7560.170	3615.7391	24EA
8	8	9	12.5	46.48865	14.54336	61.66574	15.41129	20.778989	3.7514450	770.82175	192.64109	16.8102682	12.213979	2019-10-19 17:30:00	2019-10-20 06:00:00	2019-10-20 05:36:33	2019-10-19 17:41:49	37.43866	54.05081	20.815042	675.63507	18.0672784	10.3363152	349.232911	1769.3532	848.2016	602.4171	5961.060	2947.7429	24EA
9	9	10	12.0	46.26614	18.27973	32.11570	18.11870	17.091385	3.8216336	385.38840	217.42439	-12.7199850	11.415665	2019-10-20 18:00:00	2019-10-21 06:00:00	2019-10-21 05:31:26	2019-10-20 17:53:45	34.06623	22.47428	14.994938	269.69131	-9.5791074	11.5364147	352.037669	2892.6612	1588.1801	1771.9389	12092.732	6046.6389	24EA
10	10	11	7.0	32.50895	16.11860	30.32467	15.74315	11.541404	4.8618572	212.27268	110.20205	-0.0666471	11.541211	2019-10-21 18:00:00	2019-10-22 01:00:00	2019-10-22 05:31:23	2019-10-21 17:50:34	31.44794	31.84974	4.588861	222.94815	0.7298400	4.5304502	330.636939	2621.9298	1180.7573	881.9445	9767.523	4867.6144	24EA
11	11	12	5.0	30.03292	15.91888	32.05092	15.39850	19.580955	8.0142048	160.25462	76.99248	7.9358001	17.900751	2019-10-22 18:30:00	2019-10-22 23:30:00	2019-10-23 05:33:58	2019-10-22 17:52:20	31.37587	27.22712	24.452969	136.13560	6.5159039	23.5688495	258.346279	3379.0746	1415.1158	1305.7481	7279.312	3617.1119	24EA
12	12	13	3.5	32.22310	13.93101	43.29634	21.85904	31.159851	10.3809739	151.53718	76.50664	20.8699069	23.138351	2019-10-26 19:00:00	2019-10-26 22:30:00	2019-10-27 05:34:56	2019-10-26 17:53:48	21.52444	48.03545	27.794245	168.12407	27.2363741	5.5407548	252.010060	775.6006	542.7817	562.5647	1331.474	704.7097	24EA

24EA

Settings used

Pressure timeserie

Pressure calibration

Light

Light vs Pressure

Stationay period information